GCRI · Economic Development
Regional economic development
Within countries, some cities and regions prosper while others decline, leaving gaps in income, jobs and services that persist for decades. Agglomeration, infrastructure, institutions and shocks such as mine closures or trade competition all shape where growth happens. This page sets out how regional disparities form and change, how they are measured, and what evidence serves the national and local authorities that seek to narrow them.
Regional economic development on the record.
Regional economic development: how the risk develops.
Regional specialisation and place-based policy. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Agglomeration concentrates growth
Firms and skilled workers cluster where markets, suppliers and ideas are dense, raising productivity in leading cities and leaving others behind.
Local shocks persist
When a dominant employer or industry closes, job losses spread to local services and tax bases, and recovery can take a generation.
Connectivity shapes opportunity
Roads, power, broadband and transport links determine whether lagging regions can reach markets and attract investment.
Local institutions matter
The capacity of local governments to plan, invest and deliver services affects whether national support turns into development.
Activity and hazards in Africa.
Night lights, population density and true-colour imagery over Africa, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Activity and hazards, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Activity and hazards, framed on Africa.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (7 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026
Regional economies depend on local water, land and services.
Regional analyses are designed for the Regional Systems series. Sub-national statistics follow national offices' methods.
- Sub-national data.
- Regional programme evaluations.
Regional development dashboard.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| GDP per capita by region | currency units | annual | Regional accounts | Published statistics |
| Regional unemployment rate | percent | quarterly | Labour force surveys | Published statistics |
| Night-time light intensity | radiance | monthly | Satellite night lights | OBSERVED |
| Access to markets | travel time to nearest city | per assessment | Accessibility analysis | MODELLED |
| Regional poverty rate | percent | per survey | Household surveys | ESTIMATED |
| Public investment per capita by region | currency units | annual | Budget data | Published statistics |
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction. The map on this page shows Africa; the model itself is not tied to a place.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Economic Development in Foundry
- Tested at Economic Development in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Economic Development in Reports
- Taught in Faculty of Finance and Economics in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| OECD Regional Statistics | Regional economic, social and environmental data | Public, OECD terms |
| NASA Black Marble and VIIRS night lights | Night-time light as a proxy for activity | Open, NASA data policy |
| Global Data Lab subnational indicators | Subnational development indicators from surveys | Open, licence to check |
| Malaria Atlas Project accessibility maps | Travel time to cities | Open, CC BY 4.0 |
| National census and household surveys | Regional demographics and poverty | Licence per survey |
Model regional economic development as a system. Run the cascade.
Assembled from the Nexus Standards area profile Financial and economic risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Sea-level rise
- Dam failure
- Coastal flood (set off downstream)
- Storm surge (set off downstream)
- High-tide flooding (set off downstream)
- Coastal erosion (set off downstream)
- Saltwater intrusion (set off downstream)
- Estuary flood (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Coastal habitat squeeze (set off downstream)
- Wetland loss (set off downstream)
- Mangrove loss (set off downstream)
- Groundwater flood (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Climate-driven migration and relocation (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)NRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H118Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)NRS-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-U27Universe platform profile: Sovereign Capital Nexus
Observation
- IMF data services (WEO, IFS, FSI via SDMX) (listed for reference)
- BIS Data Portal (credit, debt service, property prices, banking statistics) (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- OECD Data Explorer (SDMX API) (listed for reference)
- Macroeconomic Dashboard
- Debt service as a proportion of exports of goods and services
- Financial Soundness Indicators
Example geography
- Western Europe: 152 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
Who decides, and what the evidence serves.
Economic Development prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Ministries of planning and regional development | Where to direct investment and support | Disparity profiles; policy evaluations |
| Local governments | Which local development strategies to pursue | Local shock and connectivity evidence |
| Infrastructure agencies | Which connections would open markets for lagging regions | Accessibility evidence |
Outputs this topic produces
- Regional disparity profiles
- Evaluations of place-based policies
- Shock and recovery analyses for local economies
- Evidence notes for regional strategies
How regional economic development is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Sensitivity analysis
Which inputs move the result for regional economic development most, one at a time across their stated ranges.
What experts ask about regional development.
- Why does economic activity concentrate in some places?
- Why do some regions recover from shocks while others decline for decades?
- How do infrastructure, skills and institutions shape regional outcomes?
- When are place-based policies more effective than policies aimed at people?
- How do climate hazards and transitions affect regions differently?
What shapes regional outcomes.
Agglomeration economies explain much of spatial concentration: firms and workers are more productive when close to each other, sharing suppliers, labour pools and knowledge. This creates self-reinforcing growth in successful places and can leave others behind.
Regions specialised in a single industry are exposed to shocks to that industry. When a major employer closes or a sector declines, effects spread through local suppliers, services and public revenues. Research on regions affected by trade shocks has found that labour market effects can persist for many years, with lower employment and incomes.
Climate change adds new pressures. Coastal, agricultural and fossil-fuel-dependent regions face specific risks, and energy transitions create opportunities in some places and losses in others.
Methods and their limits.
- 01
Spatial economic data
Regional accounts, employment and business registers. Regional data are often less timely and reliable than national data.
- 02
Shift-share and specialisation analysis
Decomposing regional change into national, sectoral and local components. Descriptive, not causal.
- 03
Quasi-experimental evaluation
Assessing place-based policies using causal inference. Spillovers to neighbouring areas complicate estimates.
- 04
Agent-based models
Simulating firm and household location using complex systems methods. Validation is difficult.
Regional results are sensitive to boundaries and scale; analyses state the geography used.
How it is studied, and what the methods cannot say.
- 01Economics and development economicsGrowth diagnostics · Structural transformation analysis · Distributional incidenceApplied here
- 02EconometricsShock transmission models · Panel analysis across countries · Nowcasting with alternative dataApplied here
- 03Causal inference and impact evaluationRandomised evaluations · Evaluating place-based policies · Regression discontinuityApplied here
- 04Network science and contagionProduct space analysis · Input-output shock propagation · Financial interconnectednessApplied here
Where this page's references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
Why do regional gaps persist?
What is a place-based policy?
Can satellite night lights measure economic activity?
Should policy help places or people?
Why do some regions not recover?
Does the platform work with local governments?
How does climate change affect regional economies?
Contribute evidence from this page.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
Bring a method, a dataset or a question.
Information and enquiries are open today. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.
Legal documents.
Maps, data and figures on this site are shown for context and general information. They are not advice, forecasts or warnings; the limits of the information are set out in the Disclaimers and Data Notices.